#
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
"""
Usage instructions:
    First download the omniglot dataset 
    and put the contents of both images_background and images_evaluation in data/omniglot/ (without the root folder)

    Then, run the following:
    cd data/
    cp -r omniglot/* omniglot_resized/
    cd omniglot_resized/
    python resize_images.py
"""
from npu_bridge.npu_init import *
from PIL import Image
import glob

image_path = '*/*/'

all_images = glob.glob(image_path + '*')

i = 0

for image_file in all_images:
    im = Image.open(image_file)
    im = im.resize((28,28), resample=Image.LANCZOS)
    im.save(image_file)
    i += 1

    if i % 200 == 0:
        print(i)


